Currently, a large quantity of wearable devices, such as a smart band and implantable devices, such as a heart pacemaker have been widely applied to the daily life of users. These human body devices (a wearable device and an implantable device are collectively referred to as human body devices in this application) may sense a human body activity and parameters of an external environment, and the human body devices may summarize obtained data such that users can properly arrange various matters in the life according to the data provided by the human body devices.
However, with emergence of human body devices in a large quantity, each person may possess multiple human body devices at the same time, and therefore, how to perform effective analysis according to data provided by the multiple human body devices becomes a problem to be resolved urgently. For example, after a user tumbles, a gesture detector on the body of the user obtains gesture change data, a blood pressure monitor obtains blood pressure change data, and a heart rate monitor obtains heart rate change data. In this application, data generated by human body devices is collectively referred to as perception data. It can be seen that, the perception data obtained by these human body devices is fragmented, and a relatively accurate data analysis result cannot be provided to the user according to the fragmented data.